/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    Classifier.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.CapabilitiesHandler;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.SerializedObject;
import weka.core.Utils;

/**
 * Abstract classifier. All schemes for numeric or nominal prediction in Weka
 * extend this class. Note that a classifier MUST either implement
 * distributionForInstance() or classifyInstance().
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Len Trigg (trigg@cs.waikato.ac.nz)
 * @version $Revision: 10485 $
 */
public abstract class Classifier implements Cloneable, Serializable,
  OptionHandler, CapabilitiesHandler, RevisionHandler {

  /** for serialization */
  private static final long serialVersionUID = 6502780192411755341L;

  /** Whether the classifier is run in debug mode. */
  protected boolean m_Debug = false;

  /**
   * Generates a classifier. Must initialize all fields of the classifier that
   * are not being set via options (ie. multiple calls of buildClassifier must
   * always lead to the same result). Must not change the dataset in any way.
   * 
   * @param data set of instances serving as training data
   * @exception Exception if the classifier has not been generated successfully
   */
  public abstract void buildClassifier(Instances data) throws Exception;

  /**
   * Classifies the given test instance. The instance has to belong to a dataset
   * when it's being classified. Note that a classifier MUST implement either
   * this or distributionForInstance().
   * 
   * @param instance the instance to be classified
   * @return the predicted most likely class for the instance or
   *         Instance.missingValue() if no prediction is made
   * @exception Exception if an error occurred during the prediction
   */
  public double classifyInstance(Instance instance) throws Exception {

    double[] dist = distributionForInstance(instance);
    if (dist == null) {
      throw new Exception("Null distribution predicted");
    }
    switch (instance.classAttribute().type()) {
    case Attribute.NOMINAL:
      double max = 0;
      int maxIndex = 0;

      for (int i = 0; i < dist.length; i++) {
        if (dist[i] > max) {
          maxIndex = i;
          max = dist[i];
        }
      }
      if (max > 0) {
        return maxIndex;
      } else {
        return Instance.missingValue();
      }
    case Attribute.NUMERIC:
    case Attribute.DATE:
      return dist[0];
    default:
      return Instance.missingValue();
    }
  }

  /**
   * Predicts the class memberships for a given instance. If an instance is
   * unclassified, the returned array elements must be all zero. If the class is
   * numeric, the array must consist of only one element, which contains the
   * predicted value. Note that a classifier MUST implement either this or
   * classifyInstance().
   * 
   * @param instance the instance to be classified
   * @return an array containing the estimated membership probabilities of the
   *         test instance in each class or the numeric prediction
   * @exception Exception if distribution could not be computed successfully
   */
  public double[] distributionForInstance(Instance instance) throws Exception {

    double[] dist = new double[instance.numClasses()];
    switch (instance.classAttribute().type()) {
    case Attribute.NOMINAL:
      double classification = classifyInstance(instance);
      if (Instance.isMissingValue(classification)) {
        return dist;
      } else {
        dist[(int) classification] = 1.0;
      }
      return dist;
    case Attribute.NUMERIC:
    case Attribute.DATE:
      dist[0] = classifyInstance(instance);
      return dist;
    default:
      return dist;
    }
  }

  /**
   * Creates a new instance of a classifier given it's class name and (optional)
   * arguments to pass to it's setOptions method. If the classifier implements
   * OptionHandler and the options parameter is non-null, the classifier will
   * have it's options set.
   * 
   * @param classifierName the fully qualified class name of the classifier
   * @param options an array of options suitable for passing to setOptions. May
   *          be null.
   * @return the newly created classifier, ready for use.
   * @exception Exception if the classifier name is invalid, or the options
   *              supplied are not acceptable to the classifier
   */
  public static Classifier forName(String classifierName, String[] options)
    throws Exception {

    return (Classifier) Utils
      .forName(Classifier.class, classifierName, options);
  }

  /**
   * Creates a deep copy of the given classifier using serialization.
   * 
   * @param model the classifier to copy
   * @return a deep copy of the classifier
   * @exception Exception if an error occurs
   */
  public static Classifier makeCopy(Classifier model) throws Exception {

    return (Classifier) new SerializedObject(model).getObject();
  }

  /**
   * Creates a given number of deep copies of the given classifier using
   * serialization.
   * 
   * @param model the classifier to copy
   * @param num the number of classifier copies to create.
   * @return an array of classifiers.
   * @exception Exception if an error occurs
   */
  public static Classifier[] makeCopies(Classifier model, int num)
    throws Exception {

    if (model == null) {
      throw new Exception("No model classifier set");
    }
    Classifier[] classifiers = new Classifier[num];
    SerializedObject so = new SerializedObject(model);
    for (int i = 0; i < classifiers.length; i++) {
      classifiers[i] = (Classifier) so.getObject();
    }
    return classifiers;
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  
  public Enumeration listOptions() {

    Vector newVector = new Vector(1);

    newVector.addElement(new Option(
      "\tIf set, classifier is run in debug mode and\n"
        + "\tmay output additional info to the console", "D", 0, "-D"));
    return newVector.elements();
  }

  /**
   * Parses a given list of options. Valid options are:
   * <p>
   * 
   * -D <br>
   * If set, classifier is run in debug mode and may output additional info to
   * the console.
   * <p>
   * 
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  
  public void setOptions(String[] options) throws Exception {

    setDebug(Utils.getFlag('D', options));
  }

  /**
   * Gets the current settings of the Classifier.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  
  public String[] getOptions() {

    String[] options;
    if (getDebug()) {
      options = new String[1];
      options[0] = "-D";
    } else {
      options = new String[0];
    }
    return options;
  }

  /**
   * Set debugging mode.
   * 
   * @param debug true if debug output should be printed
   */
  public void setDebug(boolean debug) {

    m_Debug = debug;
  }

  /**
   * Get whether debugging is turned on.
   * 
   * @return true if debugging output is on
   */
  public boolean getDebug() {

    return m_Debug;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String debugTipText() {
    return "If set to true, classifier may output additional info to "
      + "the console.";
  }

  /**
   * Returns the Capabilities of this classifier. Maximally permissive
   * capabilities are allowed by default. Derived classifiers should override
   * this method and first disable all capabilities and then enable just those
   * capabilities that make sense for the scheme.
   * 
   * @return the capabilities of this object
   * @see Capabilities
   */
  
  public Capabilities getCapabilities() {
    Capabilities result = new Capabilities(this);
    result.enableAll();

    return result;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10485 $");
  }

  /**
   * runs the classifier instance with the given options.
   * 
   * @param classifier the classifier to run
   * @param options the commandline options
   */
  protected static void runClassifier(Classifier classifier, String[] options) {
    try {
      System.out.println(Evaluation.evaluateModel(classifier, options));
    } catch (Exception e) {
      if (((e.getMessage() != null) && (e.getMessage().indexOf(
        "General options") == -1))
        || (e.getMessage() == null)) {
        e.printStackTrace();
      } else {
        System.err.println(e.getMessage());
      }
    }
  }
}
